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Convolutional Neural Networks for Image Steganalysis

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dc.contributor.author Bashkirova D.
dc.date.accessioned 2018-09-19T23:10:30Z
dc.date.available 2018-09-19T23:10:30Z
dc.date.issued 2016
dc.identifier.issn 2191-1630
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/146003
dc.description.abstract © 2016, Springer Science+Business Media New York.Mathematical models based on human neuronal network behavior have recently become extremely popular and arouse interest as a solution of various computer vision problems. One of these models—Convolutional Neural Network—has been proven to be very efficient for object recognition problems and resembles principles of visual processing held by animal visual cortex. In this research, we propose a new approach to performing steganalysis on JPEG images using Convolutional Neural Networks. This approach allows to detect hidden embedding without computing features of an image predefined by empirical observations and obtain results comparable to state of the art methods of JPEG image steganalysis.
dc.relation.ispartofseries BioNanoScience
dc.subject Convolutional neural network
dc.subject Image processing
dc.subject Stegananlysis
dc.title Convolutional Neural Networks for Image Steganalysis
dc.type Article
dc.relation.ispartofseries-issue 3
dc.relation.ispartofseries-volume 6
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 246
dc.source.id SCOPUS21911630-2016-6-3-SID84983656092


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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